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datasets.py
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"""
Functions to load CIFAR-10 and SVHN data.
Most of the codes in this file are excerpted from the original work
https://github.com/QinbinLi/MOON/blob/main/datasets.py
"""
import logging
import os
import os.path
import numpy as np
import torch.utils.data as data
import torchvision
from PIL import Image
from torchvision.datasets import CIFAR10, SVHN
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
IMG_EXTENSIONS = (
".jpg",
".jpeg",
".png",
".ppm",
".bmp",
".pgm",
".tif",
".tiff",
".webp",
)
def mkdirs(dirpath):
try:
os.makedirs(dirpath)
except Exception:
pass
class SVHN_truncated(data.Dataset):
def __init__(
self,
root,
dataidxs=None,
split="train",
transform=None,
target_transform=None,
download=False,
):
self.root = root
self.dataidxs = dataidxs
self.split = split
self.transform = transform
self.target_transform = target_transform
self.download = download
self.data, self.target = self.__build_truncated_dataset__()
def __build_truncated_dataset__(self):
svhn_dataobj = SVHN(self.root, self.split, self.transform, self.target_transform, self.download)
data = svhn_dataobj.data
target = np.array(svhn_dataobj.labels)
if self.dataidxs is not None:
data = data[self.dataidxs]
target = target[self.dataidxs]
return data, target
def truncate_channel(self, index):
for i in range(index.shape[0]):
gs_index = index[i]
self.data[gs_index, :, :, 1] = 0.0
self.data[gs_index, :, :, 2] = 0.0
def __getitem__(self, index):
img, target = self.data[index], int(self.target[index])
img = Image.fromarray(np.transpose(img, (1, 2, 0)))
if self.transform is not None:
img1 = self.transform(img)
img2 = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img1, img2, target, index
def __len__(self):
return len(self.data)
class CIFAR10_truncated(data.Dataset):
def __init__(
self,
root,
dataidxs=None,
train=True,
transform=None,
target_transform=None,
download=False,
):
self.root = root
self.dataidxs = dataidxs
self.train = train
self.transform = transform
self.target_transform = target_transform
self.download = download
self.data, self.target = self.__build_truncated_dataset__()
def __build_truncated_dataset__(self):
cifar_dataobj = CIFAR10(self.root, self.train, self.transform, self.target_transform, self.download)
if torchvision.__version__ == "0.2.1":
if self.train:
data, target = cifar_dataobj.train_data, np.array(cifar_dataobj.train_labels)
else:
data, target = cifar_dataobj.test_data, np.array(cifar_dataobj.test_labels)
else:
data = cifar_dataobj.data
target = np.array(cifar_dataobj.targets)
if self.dataidxs is not None:
data = data[self.dataidxs]
target = target[self.dataidxs]
return data, target
def truncate_channel(self, index):
for i in range(index.shape[0]):
gs_index = index[i]
self.data[gs_index, :, :, 1] = 0.0
self.data[gs_index, :, :, 2] = 0.0
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.target[index]
# img = Image.fromarray(img)
# print("cifar10 img:", img)
# print("cifar10 target:", target)
if self.transform is not None:
img1 = self.transform(img)
img2 = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img1, img2, target, index
def __len__(self):
return len(self.data)